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Jens Kober

34 accepted papers

2026

ILeSiA: Interactive Learning of Robot Situational Awareness from Camera Input

Petr Vanc, Giovanni Franzese, Jan Kristof Behrens, Cosimo Della Santina, Karla Stepanova, Jens Kober +1
ICRA 2026poster

Learning from demonstration is a promising way to teach robots new skills. However, a central challenge in executing acquired skills is the ability to recognize faults and prevent failures. This is essential since the demonstrations usually cover only a limited number of mostly successful cases. Dur…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

LLM-Guided Task and Affordance-Level Exploration in Reinforcement Learning

Jelle Douwe Luijkx, Runyu Ma, Zlatan Ajanovic, Jens Kober
ICRA 2026poster

Reinforcement learning (RL) is a promising approach for robotic manipulation, but it can suffer from low sample efficiency and requires extensive exploration of large state-action spaces. Recent methods leverage the commonsense knowledge and reasoning abilities of large language models (LLMs) to gui…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2026

Sequentially Teaching Sequential Tasks (ST)²: Teaching Robots Long-Horizon Manipulation Skills

Zlatan Ajanovic, Ravi Prakash, Leandro de Souza Rosa, Jens Kober
ICRA 2026poster

Learning from demonstration has proved itself useful for teaching robots complex skills with high sample efficiency. However, teaching long-horizon tasks with multiple skills is challenging as deviations tend to accumulate, the distributional shift becomes more evident, and human teachers become fat…

Cited by 0Scholar ↗
2026

Set-Supervised Diffusion Policy: Learning Action-Chunking Diffusion through Corrections

Zhaoting Li, Gang Chen, Javier Alonso-Mora, Cosimo Della Santina, Jens Kober
RSS 2026poster

Diffusion policies have recently emerged as a powerful framework for robotic manipulation. However, like other behavior cloning methods, they remain vulnerable to distributional shift, often requiring human-in-the-loop interventions to correct failures during deployment. These interactions naturally…

Cited by 0Source ↗code ↗Scholar ↗
2025

ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models

Runyu Ma, Jelle Luijkx, Zlatan Ajanovic, Jens Kober
ICRA 2025

In robot manipulation, Reinforcement Learning (RL) often suffers from low sample efficiency and uncertain convergence, especially in large observation and action spaces. Foundation Models (FMs) offer an alternative, demonstrating promise in zero-shot and few-shot settings. However, they can be unrel

Cited by 25Source ↗code ↗Scholar ↗
2025

Explosive Jumping with Rigid and Articulated Soft Quadrupeds via Example Guided Reinforcement Learning

Georgios Apostolides, Wei Pan, Jens Kober, Cosimo Della Santina, Jiatao Ding
IROS 2025

Achieving controlled jumping behaviour for a quadruped robot is a challenging task, especially when introducing passive compliance in mechanical design. This study addresses this challenge via imitation-based deep reinforcement learning with a progressive training process. To start, we learn the jum

Cited by 1Source ↗Scholar ↗
2025

Impedance Primitive-Augmented Hierarchical Reinforcement Learning for Sequential Tasks

Amin Berjaoui Tahmaz, Ravi Prakash, Jens Kober
ICRA 2025

This paper presents an Impedance Primitive-augmented hierarchical reinforcement learning framework for efficient robotic manipulation in sequential contact tasks. We leverage this hierarchical structure to sequentially execute behavior primitives with variable stiffness control capabilities for cont

Cited by 3Source ↗Scholar ↗
2025

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation

Anish Abhijit Diwan, Julen Urain, Jens Kober, Jan Peters
ICLR 2025poster

This paper introduces a new imitation learning framework based on energy-based generative models capable of learning complex, physics-dependent, robot motion policies through state-only expert motion trajectories. Our algorithm, called Noise-conditioned Energy-based Annealed Rewards (NEAR), construc…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Demonstrating Adaptive Mobile Manipulation in Retail Environments

Max Spahn, Corrado Pezzato, Chadi Salmi, Rick Dekker, Cong Wang, Christian Pek +4
RSS 2024poster

Although autonomous robots have great potential to boost efficiency and throughput across the whole retail chain, they are mostly being deployed in large warehouses and distribution centers. Deploying robots in stores with customers, such as supermarkets, requires substantially more development effo…

Cited by 5Source ↗PDF ↗Scholar ↗
2024

Learning Multi-Reference Frame Skills from Demonstration with Task-Parameterized Gaussian Processes

Mariano Ramírez Montero, Giovanni Franzese, Jens Kober, Cosimo Della Santina
IROS 2024poster

A central challenge in Learning from Demonstration is to generate representations that are adaptable and can generalize to unseen situations. This work proposes to learn such a representation without using task-specific heuristics within the context of multi-reference frame skill learning by superim…

Cited by 1Source ↗Scholar ↗
2024

ROME: Robust Multi-Modal Density Estimator

Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora, Arkady Zgonnikov, Jens Kober
IJCAI 2024poster

The estimation of probability density functions is a fundamental problem in science and engineering. However, common methods such as kernel density estimation (KDE) have been demonstrated to lack robustness, while more complex methods have not been evaluated in multi-modal estimation problems. In th…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2024

Robust Jumping With an Articulated Soft Quadruped Via Trajectory Optimization and Iterative Learning

Jiatao Ding, Mees A. van Löben Sels, Franco Angelini, Jens Kober, Cosimo Della Santina
RA-L 2024

Quadrupeds deployed in real-world scenarios need to be robust to unmodelled dynamic effects. In this work, we aim to increase the robustness of quadrupedal periodic forward jumping (i.e., pronking) by unifying cutting-edge model-based trajectory optimization and iterative learning control. Using a r

Cited by 16Source ↗Scholar ↗
2024

Two-Stage Learning of Highly Dynamic Motions with Rigid and Articulated Soft Quadrupeds

Francecso Vezzi, Jiatao Ding, Antonin Raffin, Jens Kober, Cosimo Della Santina
ICRA 2024poster

Controlled execution of dynamic motions in quadrupedal robots, especially those with articulated soft bodies, presents a unique set of challenges that traditional methods struggle to address efficiently. In this study, we tackle these issues by relying on a simple yet effective two-stage learning fr…

Cited by 9Source ↗code ↗Scholar ↗
2023

Probabilistic Risk Assessment for Chance-Constrained Collision Avoidance in Uncertain Dynamic Environments

Khaled A. Mustafa, Oscar de Groot, Xinwei Wang, Jens Kober, Javier Alonso-Mora
ICRA 2023poster

Balancing safety and efficiency when planning in crowded scenarios with uncertain dynamics is challenging where it is imperative to accomplish the robot's mission without incurring any safety violations. Typically, chance constraints are incorporated into the planning problem to provide probabilisti…

Cited by 11Source ↗Scholar ↗
2021

DeepKoCo: Efficient latent planning with a task-relevant Koopman representation

Bas van der Heijden, Laura Ferranti, Jens Kober, Robert Babuška
IROS 2021poster

This paper presents DeepKoCo, a novel modelbased agent that learns a latent Koopman representation from images. This representation allows DeepKoCo to plan efficiently using linear control methods, such as linear model predictive control. Compared to traditional agents, DeepKoCo learns taskrelevant…

Cited by 4Source ↗Scholar ↗
2021

ILoSA: Interactive Learning of Stiffness and Attractors

Giovanni Franzese, Anna Mészáros, Luka Peternel, Jens Kober
IROS 2021poster

Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to learn variable impedance policies where both the Cartesian stiffness and the attractor can be learned from human demons…

Cited by 36Source ↗code ↗Scholar ↗
2021

Imitation Learning with Inconsistent Demonstrations through Uncertainty-based Data Manipulation

Peter Valletta, Rodrigo Pérez-Dattari, Jens Kober
ICRA 2021poster

Aleatoric uncertainty estimation, based on the observed training data, is applied for the detection of conflicts in a demonstration data set. The particular focus of this paper is the resolution of conflicting data resulting from scenarios with equivalent action choices, such as obstacle avoidance,…

Cited by 4Source ↗Scholar ↗
2020

Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks

Linda van der Spaa, Michael Gienger, Tamas Bates, Jens Kober
ICRA 2020poster

This paper presents a method to incorporate ergonomics into the optimization of action sequences for bi-manual human-robot cooperation tasks with continuous physical interaction. Our first contribution is a novel computational model of the human that allows prediction of an ergonomics assessment cor…

Cited by 62Source ↗Scholar ↗
2019

Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach

Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar, Jens Kober
ICRA 2019poster

Deep Reinforcement Learning (DRL) has become a powerful methodology to solve complex decision-making problems. However, DRL has several limitations when used in real-world problems (e.g., robotics applications). For instance, long training times are required and cannot be accelerated in contrast to…

Cited by 28Source ↗Scholar ↗
2018

Human-Robot Cooperative Object Manipulation with Contact Changes

Michael Gienger, Dirk Ruiken, Tamas Bates, Mohamed Regaieg, Michael MeiBner, Jens Kober +2
IROS 2018poster

This paper presents a system for cooperatively manipulating large objects between a human and a robot. This physical interaction system is designed to handle, transport, or manipulate large objects of different shapes in cooperation with a human. Unique points are the bi-manual physical cooperation,…

Cited by 31Source ↗Scholar ↗
2018

Integrating State Representation Learning Into Deep Reinforcement Learning

Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuska
RA-L 2018

Most deep reinforcement learning techniques are unsuitable for robotics, as they require too much interaction time to learn useful, general control policies. This problem can be largely attributed to the fact that a state representation needs to be learned as a part of learning control policies, whi

Cited by 119Source ↗Scholar ↗
2018

Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations

Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters
RA-L 2018

In this letter, we introduce Mixture of Attractors, a novel movement primitive representation that allows for learning complex object-relative movements. The movement primitive representation inherently supports multiple coordinate frames, enabling the system to generalize a skill to unseen object p

Cited by 19Source ↗Scholar ↗
2016

Improved deep reinforcement learning for robotics through distribution-based experience retention

Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuška
IROS 2016poster

Recent years have seen a growing interest in the use of deep neural networks as function approximators in reinforcement learning. In this paper, an experience replay method is proposed that ensures that the distribution of the experiences used for training is between that of the policy and a uniform…

Cited by 54Source ↗Scholar ↗
2016

Probabilistic decomposition of sequential force interaction tasks into Movement Primitives

Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters
IROS 2016poster

Learning sequential force interaction tasks from kinesthetic demonstrations is a promising approach to transfer human manipulation abilities to a robot. In this paper we propose a novel concept to decompose such demonstrations into a set of Movement Primitives (MPs). The decomposition is based on a…

Cited by 18Source ↗Scholar ↗
2015

Probabilistic progress prediction and sequencing of concurrent movement primitives

Simon Manschitz, Jens Kober, Michael Gienger, Jan Peters
IROS 2015poster

Classical approaches towards learning coordinated movement tasks often represent a movement in a sequential and exclusive fashion. Introducing concurrency allows to decompose such tasks into a number of separate sequences, for instance for two different end-effectors. While this results in a compact…

Cited by 9Source ↗Scholar ↗